A decision tree is a decision analysis tool that uses a tree-like model of decisions and possible consequences to predict the value of a target based on a set of predictor variables. Because a decision tree does not require as many model assumptions compared to traditional parametric models and because it is easy to interpret and understand, it is a popular data mining tool for classification and prediction in business analytics.
A decision tree may include a root node connected to one or more interior nodes that in turn are connected to one or more leaf nodes. The leaf nodes of a decision tree can represent a segment of the data such that the leaf nodes provide a distribution of a target variable. The segments of data are defined by the values of the predictors by the path from the root node to the leaf node. A path from a root node to a leaf node can be considered a rule or customer profile, and the entire decision tree may represent a set of rules or a set of customer profiles.
Decision trees can be regression trees or classification trees. A regression tree may be used to predict the values of continuous target variable while a classification tree may be used to classify the values of categorical targets into target categories.